Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems.

Industrial contexts, typically characterized by highly unstructured environments, where task sequences are difficult to hard-code and unforeseen events occur daily (e.g., oil and gas, energy generation, aeronautics) cannot completely rely upon automation to substitute the human dexterity and judgment skills. Robots operating in these conditions have the common requirement of being able to deploy appropriate behaviours in highly dynamic and unpredictable environments, while aiming to achieve a more natural human-robot interaction and a broad range of acceptability in providing useful and efficient services. The goal of this paper is to introduce a deliberative framework able to acquire, reuse and instantiate a collection of behaviours that promote an extension of the autonomy periods of mobile robotic platforms, with a focus on maintenance, repairing and overhaul applications. Behavior trees are employed to design the robotic system’s high-level deliberative intelligence, which integrates: social behaviors, aiming to capture the human’s emotional state and intention; the ability to either perform or support various process tasks; seamless planning and execution of human-robot shared work plans. In particular, the modularity, reactiveness and deliberation capacity that characterize the behaviour tree formalism are leveraged to interpret the human’s health and cognitive load for supporting her/him, and to complete a shared mission by collaboration or complete take-over. By enabling mobile robotic platforms to take-over risky jobs which the human cannot, should not or do not want to perform the proposed framework bears high potential to significantly improve the safety, productivity and efficiency in harsh working environments.

Nowadays, the role of hybridization within the wider manufacturing ecosystem gains significant momentum with multiple commercial solutions already available on the market. Despite the very promising benefits of combining and selectively exploiting the advantages of additive and subtractive technologies on the same machine, hybrid additive manufacturing is far from reaching its full potential. One of the central limitations of existing hybrid process chains is the lack of a harmonized, structured and automated workflows to support an adaptive manufacturing strategy. This work is motivated by the need to bridge this gap and to capture the logic behind an adaptive hybrid process chain with the aim to support the achievement of enhanced product quality and improved operational efficiency in hybrid additive manufacturing. The paper discusses the implementation of a hybrid CAx platform and the underlying methodology aiming at the dynamic reduction of variabilities associated with the laser metal deposition process. The hybrid workflow identifies the most adapted sequence and planning of additive and subtractive operations while considering part inspection as an in-envelope functionality to quantify the geometrical and dimensional part deviations and to trigger the regenerative mechanism. The methodology is demonstrated on a hybrid machine by deploying laser ablation for the in situ removal of build deviations and an adapted deposition operation as part of a regenerative strategy leading to higher part confidence.